






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class UpperCAmelCase_       (     SCREAMING_SNAKE_CASE_					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      KandinskyVaaInpaintPipeline
      UpperCamelCase_             =      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCamelCase_             =      [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCamelCase_             =      [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCamelCase_             =      False






      @property
      def 					A__   (			self      :		Tuple		)	->	Tuple:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		Union[str, Any]		)	->	List[Any]:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		int		)	->	Tuple:


       '''simple docstring'''

       return self.time_input_dim






      @property
      def 					A__   (			self      :		int		)	->	List[Any]:


       '''simple docstring'''

       return self.time_input_dim * 4






      @property
      def 					A__   (			self      :		Optional[Any]		)	->	Dict:


       '''simple docstring'''

       return 100






      @property
      def 					A__   (			self      :		int		)	->	Optional[Any]:


       '''simple docstring'''

       torch.manual_seed(0		)

       lowercase       : str						={
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       lowercase       : Optional[Any]						=UNetaDConditionModel(**snake_case__		)
       return model






      @property
      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






      @property
      def 					A__   (			self      :		List[str]		)	->	Union[str, Any]:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase       : Dict						=VQModel(**self.dummy_movq_kwargs		)
       return model






      def 					A__   (			self      :		List[str]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[str]						=self.dummy_unet
       lowercase       : Optional[int]						=self.dummy_movq

       lowercase       : Union[str, Any]						=DDIMScheduler(
           num_train_timesteps=1000  ,   beta_schedule='''linear'''  ,   beta_start=0.0_0_0_8_5  ,   beta_end=0.0_1_2  ,   clip_sample=snake_case__  ,   set_alpha_to_one=snake_case__  ,   steps_offset=1  ,   prediction_type='''epsilon'''  ,   thresholding=snake_case__  ,   )

       lowercase       : Dict						={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Union[str, Any]=0		)	->	int:


       '''simple docstring'''

       lowercase       : int						=floats_tensor((1, self.text_embedder_hidden_size)  ,   rng=random.Random(snake_case__		)		).to(snake_case__		)
       lowercase       : str						=floats_tensor((1, self.text_embedder_hidden_size)  ,   rng=random.Random(seed + 1		)		).to(
           snake_case__		)
       # create init_image
       lowercase       : str						=floats_tensor((1, 3, 64, 64)  ,   rng=random.Random(snake_case__		)		).to(snake_case__		)
       lowercase       : Tuple						=image.cpu().permute(0  ,   2  ,   3  ,   1		)[0]
       lowercase       : Optional[Any]						=Image.fromarray(np.uinta(snake_case__		)		).convert('''RGB'''		).resize((256, 256)		)
       # create mask
       lowercase       : Optional[int]						=np.ones((64, 64)  ,   dtype=np.floataa		)
       lowercase       : Any						=0

       if str(snake_case__		).startswith('''mps'''		):
        lowercase       : Optional[int]						=torch.manual_seed(snake_case__		)
       else:
        lowercase       : List[Any]						=torch.Generator(device=snake_case__		).manual_seed(snake_case__		)
       lowercase       : Any						={
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs






      def 					A__   (			self      :		Dict		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						='''cpu'''

       lowercase       : Union[str, Any]						=self.get_dummy_components()

       lowercase       : List[str]						=self.pipeline_class(**snake_case__		)
       lowercase       : Union[str, Any]						=pipe.to(snake_case__		)

       pipe.set_progress_bar_config(disable=snake_case__		)

       lowercase       : Any						=pipe(**self.get_dummy_inputs(snake_case__		)		)
       lowercase       : Optional[int]						=output.images

       lowercase       : List[Any]						=pipe(
           **self.get_dummy_inputs(snake_case__		)  ,   return_dict=snake_case__  ,   )[0]

       lowercase       : Any						=image[0, -3:, -3:, -1]
       lowercase       : Dict						=image_from_tuple[0, -3:, -3:, -1]

       print(f'image.shape {image.shape}'		)

       assert image.shape == (1, 64, 64, 3)

       lowercase       : str						=np.array(
           [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]		)

       assert (
           np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
       ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
       ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       super().test_inference_batch_single_identical(expected_max_diff=3e-3		)





@slow
@require_torch_gpu
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def 					A__   (			self      :		Any		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : int						=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''		)

       lowercase       : Union[str, Any]						=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
       lowercase       : str						=np.ones((768, 768)  ,   dtype=np.floataa		)
       lowercase       : List[str]						=0

       lowercase       : Optional[int]						='''a hat'''

       lowercase       : Any						=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''  ,   torch_dtype=torch.floataa		)
       pipe_prior.to(snake_case__		)

       lowercase       : str						=KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''  ,   torch_dtype=torch.floataa		)
       lowercase       : Optional[Any]						=pipeline.to(snake_case__		)
       pipeline.set_progress_bar_config(disable=snake_case__		)

       lowercase       : Optional[int]						=torch.Generator(device='''cpu'''		).manual_seed(0		)
       lowercase	,		lowercase       : Tuple						=pipe_prior(
           snake_case__  ,   generator=snake_case__  ,   num_inference_steps=5  ,   negative_prompt=''''''  ,   ).to_tuple()

       lowercase       : Union[str, Any]						=pipeline(
           image=snake_case__  ,   mask_image=snake_case__  ,   image_embeds=snake_case__  ,   negative_image_embeds=snake_case__  ,   generator=snake_case__  ,   num_inference_steps=100  ,   height=768  ,   width=768  ,   output_type='''np'''  ,   )

       lowercase       : Optional[Any]						=output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(snake_case__  ,   snake_case__		)




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A						=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default='cifar10'     ,metadata={'help': 'Name of a dataset from the datasets package'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The column name of the images in the files.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the training data.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the validation data.'}			)
		lowerCAmelCase_     =   field(
		    default=0.1_5     ,metadata={'help': 'Percent to split off of train for validation.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  {}
				if self.train_dir is not None:
						_lowerCamelCase				   =  self.train_dir
				if self.validation_dir is not None:
						_lowerCamelCase				   =  self.validation_dir
				_lowerCamelCase				   =  data_files if data_files else None





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
		lowerCAmelCase_     =   field(
		    default='main'     ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Name or path of preprocessor config.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=0.7_5     ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=1e-3     ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)



def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  torch.stack([example['pixel_values'] for example in examples]							)
		return {"pixel_values": pixel_values}


def 	lowerCamelCase					(	)   ->    Dict:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_lowerCamelCase				   =  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
		if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'			,       UpperCamelCase			,       UpperCamelCase							)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout							)]			,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_lowerCamelCase				   =  training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase							)
		transformers.utils.logging.set_verbosity(UpperCamelCase							)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
		logger.info(F"""Training/evaluation parameters {training_args}"""							)

		# Detecting last checkpoint.
		_lowerCamelCase				   =  None
		if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
				_lowerCamelCase				   =  get_last_checkpoint(training_args.output_dir							)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'							)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
		_lowerCamelCase				   =  load_dataset(
		    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		_lowerCamelCase				   =  None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split			,       UpperCamelCase							) and data_args.train_val_split > 0.0:
				_lowerCamelCase				   =  ds['train'].train_test_split(data_args.train_val_split							)
				_lowerCamelCase				   =  split['train']
				_lowerCamelCase				   =  split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_lowerCamelCase				   =  {
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.config_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'							)
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""							)
						config.update_from_string(model_args.config_overrides							)
						logger.info(F"""New config: {config}"""							)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }							)

		# create image processor
		if model_args.image_processor_name:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path							)			,       config=UpperCamelCase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
		else:
				logger.info('Training new model from scratch'							)
				_lowerCamelCase				   =  ViTMAEForPreTraining(UpperCamelCase							)

		if training_args.do_train:
				_lowerCamelCase				   =  ds['train'].column_names
		else:
				_lowerCamelCase				   =  ds['validation'].column_names

		if data_args.image_column_name is not None:
				_lowerCamelCase				   =  data_args.image_column_name
		elif "image" in column_names:
				_lowerCamelCase				   =  'image'
		elif "img" in column_names:
				_lowerCamelCase				   =  'img'
		else:
				_lowerCamelCase				   =  column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_lowerCamelCase				   =  image_processor.size['shortest_edge']
		else:
				_lowerCamelCase				   =  (image_processor.size['height'], image_processor.size['width'])
		_lowerCamelCase				   =  Compose(
		    [
		        Lambda(lambda UpperCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
		        RandomResizedCrop(UpperCamelCase			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC							),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std							),
		    ]							)

		def preprocess_images(UpperCamelCase     :				Tuple							):
				_lowerCamelCase				   =  [transforms(UpperCamelCase							) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'							)
				if data_args.max_train_samples is not None:
						_lowerCamelCase				   =  ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
				# Set the training transforms
				ds["train"].set_transform(UpperCamelCase							)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'							)
				if data_args.max_eval_samples is not None:
						_lowerCamelCase				   =  (
						    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
						)
				# Set the validation transforms
				ds["validation"].set_transform(UpperCamelCase							)

		# Compute absolute learning rate
		_lowerCamelCase				   =  (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_lowerCamelCase				   =  training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		_lowerCamelCase				   =  Trainer(
		    model=UpperCamelCase			,       args=UpperCamelCase			,       train_dataset=ds['train'] if training_args.do_train else None			,       eval_dataset=ds['validation'] if training_args.do_eval else None			,       tokenizer=UpperCamelCase			,       data_collator=UpperCamelCase			,       )

		# Training
		if training_args.do_train:
				_lowerCamelCase				   =  None
				if training_args.resume_from_checkpoint is not None:
						_lowerCamelCase				   =  training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_lowerCamelCase				   =  last_checkpoint
				_lowerCamelCase				   =  trainer.train(resume_from_checkpoint=UpperCamelCase							)
				trainer.save_model()
				trainer.log_metrics('train'			,       train_result.metrics							)
				trainer.save_metrics('train'			,       train_result.metrics							)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_lowerCamelCase				   =  trainer.evaluate()
				trainer.log_metrics('eval'			,       UpperCamelCase							)
				trainer.save_metrics('eval'			,       UpperCamelCase							)

		# Write model card and (optionally) push to hub
		_lowerCamelCase				   =  {
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**UpperCamelCase							)
		else:
				trainer.create_model_card(**UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Dict:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()